| 引用本文: | 胡钊政,孙莹妹,李祎承.路面路标高精度地图构建与多尺度车辆定位[J].哈尔滨工业大学学报,2019,51(9):149.DOI:10.11918/j.issn.0367-6234.201806013 |
| HU Zhaozheng,SUN Yingmei,LI Yicheng.High definition map construction from pavement landmarks for multi-scale vehicle localization[J].Journal of Harbin Institute of Technology,2019,51(9):149.DOI:10.11918/j.issn.0367-6234.201806013 |
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| 摘要: |
| 为提高智能车定位精度,提出一种利用路面标志构建高精度地图的方法,并在制作的地图基础上提出多尺度车辆定位算法. 以路面路标为核心构建高精度视觉地图,地图中每个路标均包含路标视觉特征、几何结构信息,以及其在参考坐标系的精确的位置关系. 在定位过程中,首先通过GPS粗匹配计算车辆位置在地图中的位置范围；然后匹配地图中的视觉特征实现路标级定位；最后通过地图中的路标的几何结构信息与参考位置关系实现车辆位置的精确计算,从而实现基于路面路标高精度地图的车辆多尺度定位. 针对某大学校园约3.4 km的道路路面标志（包括路面直行箭头、右转箭头、井盖等）进行高精度地图构建,并以之为基础实现车辆定位. 定位实验结果表明:算法平均定位误差为12.5 cm,最大定位误差为23.3 cm. 定位采取先制图后定位以及多尺度匹配的策略为高精度智能车定位建议了一种新的方法. |
| 关键词: 智能车 车辆定位 路面路标 多尺度定位 高精度视觉地图 |
| DOI：10.11918/j.issn.0367-6234.201806013 |
| 分类号:U495 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(51679181)；湖北省技术创新项目重大专项(2016AAA007)；河北省青年拔尖人才计划 |
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| High definition map construction from pavement landmarks for multi-scale vehicle localization |
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HU Zhaozheng1,2,SUN Yingmei1,LI Yicheng2
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(1.School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China; 2. Intelligent Transportation Systems (ITS) Research Center, Wuhan University of Technology, Wuhan 430063, China)
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| Abstract: |
| A multi-scale vehicle localization method was proposed to improve the localization accuracy of intelligent vehicles by constructing high definition maps from pavement landmarks. A high definition visual map was constructed based on pavement landmarks, where each landmark contains visual features, geometric structure information, and the positional coordinates in a reference coordinate system. Based on the constructed map, a coarse location of the vehicle was estimated through GPS matching, which was then improved by matching the visual features in the map to achieve landmark-level localization. Finally, the accurate and absolute position was achieved from the landmark geometry and its reference position, which thus could realize the multi-scale localization of the vehicle by high definition maps from pavement landmarks. A 3.4 km-long route of a university campus (including right-turn arrow, straight-way arrow, manhole, and so on) was taken as an example to construct a high definition visual map and then realize vehicle localization. Results showed that the mean and the max localization errors were 12.5 cm and 23.3 cm, respectively. The proposed mapping strategy based multi-scale localization method provides a new solution for intelligent vehicle localization. |
| Key words: intelligent vehicle vehicle localization pavement landmark multi-scale localization high definition visual map |
